Welcome to the Notebook

Alaa Ahmed Elshafei

Importing modules

Task 1

import pandas as pd
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt 
print('modules are imported')

modules are imported

Task 1.1:

Loading the Dataset

dataset_url='https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv'
df=pd.read_csv(dataset_url)

Task 1.2:

let's check the dataframe

df.head()

	Date	Country	Confirmed	Recovered	Deaths
0	2020-01-22	Afghanistan	0	0	0
1	2020-01-23	Afghanistan	0	0	0
2	2020-01-24	Afghanistan	0	0	0
3	2020-01-25	Afghanistan	0	0	0
4	2020-01-26	Afghanistan	0	0	0

df.tail()

	Date	Country	Confirmed	Recovered	Deaths
161563	2022-04-12	Zimbabwe	247094	0	5460
161564	2022-04-13	Zimbabwe	247160	0	5460
161565	2022-04-14	Zimbabwe	247208	0	5462
161566	2022-04-15	Zimbabwe	247237	0	5462
161567	2022-04-16	Zimbabwe	247237	0	5462

let's check the shape of the dataframe

df.shape

(161568, 5)

Task 2.1 :

let's do some preprocessing

df = df[df.Confirmed > 0]

df

	Date	Country	Confirmed	Recovered	Deaths
33	2020-02-24	Afghanistan	5	0	0
34	2020-02-25	Afghanistan	5	0	0
35	2020-02-26	Afghanistan	5	0	0
36	2020-02-27	Afghanistan	5	0	0
37	2020-02-28	Afghanistan	5	0	0
...	...	...	...	...	...
161563	2022-04-12	Zimbabwe	247094	0	5460
161564	2022-04-13	Zimbabwe	247160	0	5460
161565	2022-04-14	Zimbabwe	247208	0	5462
161566	2022-04-15	Zimbabwe	247237	0	5462
161567	2022-04-16	Zimbabwe	247237	0	5462

148455 rows × 5 columns

let's see data related to a country for example Italy

df[df.Country ==  'Italy']

	Date	Country	Confirmed	Recovered	Deaths
70185	2020-01-31	Italy	2	0	0
70186	2020-02-01	Italy	2	0	0
70187	2020-02-02	Italy	2	0	0
70188	2020-02-03	Italy	2	0	0
70189	2020-02-04	Italy	2	0	0
...	...	...	...	...	...
70987	2022-04-12	Italy	15404809	0	161032
70988	2022-04-13	Italy	15467395	0	161187
70989	2022-04-14	Italy	15533012	0	161336
70990	2022-04-15	Italy	15595302	0	161469
70991	2022-04-16	Italy	15659835	0	161602

807 rows × 5 columns

let's see Global spread of Covid19

fig = px.choropleth(df , locations= 'Country' , locationmode= 'country names' , color='Confirmed', animation_frame= 'Date')
fig.update_layout(title_text = 'Global Spread of COVID-19 ')
fig.show()

Task 2.2 : Exercise

Let's see Global deaths of Covid19

fig = px.choropleth(df , locations= 'Country' , locationmode= 'country names' , color='Deaths', animation_frame= 'Date')
fig.update_layout(title_text = 'Global Spread of COVID-19 ')
fig.show()

Task 3.1:

Let's Visualize how intensive the Covid19 Transmission has been in each of the country

let's start with an example:

df_china = df[df.Country ==  'China']
df_china.head()

	Date	Country	Confirmed	Recovered	Deaths
30192	2020-01-22	China	548	28	17
30193	2020-01-23	China	643	30	18
30194	2020-01-24	China	920	36	26
30195	2020-01-25	China	1406	39	42
30196	2020-01-26	China	2075	49	56

let's select the columns that we need

df_china=df_china[['Date','Confirmed']]
df_china

	Date	Confirmed
30192	2020-01-22	548
30193	2020-01-23	643
30194	2020-01-24	920
30195	2020-01-25	1406
30196	2020-01-26	2075
...	...	...
31003	2022-04-12	1655477
31004	2022-04-13	1681437
31005	2022-04-14	1705231
31006	2022-04-15	1759128
31007	2022-04-16	1760211

816 rows × 2 columns

calculating the first derivation of confrimed column

df_china['Infection Rate'] = df_china['Confirmed'].diff()

df_china.head()

	Date	Confirmed	Infection Rate
30192	2020-01-22	548	NaN
30193	2020-01-23	643	95.0
30194	2020-01-24	920	277.0
30195	2020-01-25	1406	486.0
30196	2020-01-26	2075	669.0

px.line(df_china , x= 'Date', y= ['Confirmed','Infection Rate' ])

df_china['Infection Rate'].max()

77402.0

Task 3.2:

Let's Calculate Maximum infection rate for all of the countries

df.head()

	Date	Country	Confirmed	Recovered	Deaths
33	2020-02-24	Afghanistan	5	0	0
34	2020-02-25	Afghanistan	5	0	0
35	2020-02-26	Afghanistan	5	0	0
36	2020-02-27	Afghanistan	5	0	0
37	2020-02-28	Afghanistan	5	0	0

Countries = list(df['Country'].unique())
max_Infection_Rate = []
for c in Countries :
    MIR = df[df.Country == c ].Confirmed.diff().max()
    max_Infection_Rate.append(MIR)

Task 3.3:

let's create a new Dataframe

df_MIR = pd.DataFrame()
df_MIR['Country'] = Countries
df_MIR['Max Infection Rate']=max_Infection_Rate
df_MIR.head()

	Country	Max Infection Rate
0	Afghanistan	3243.0
1	Albania	4789.0
2	Algeria	2521.0
3	Andorra	2313.0
4	Angola	5035.0

Let's plot the barchart : maximum infection rate of each country

px.bar (df_MIR , x= 'Country' , y= 'Max Infection Rate' ,color='Country',title='Global Max Infection Rate',log_y=True   )

Task 4: Let's See how National Lockdowns Impacts Covid19 transmission in Italy

COVID19 pandemic lockdown in Italy

On 9 March 2020, the government of Italy under Prime Minister Giuseppe Conte imposed a national quarantine, restricting the movement of the population except for necessity, work, and health circumstances, in response to the growing pandemic of COVID-19 in the country. source

italy_lockdown_start_date = '2020-03-09'
italy_lockdown_a_month_later = '2020-04-09'

df.head()

	Date	Country	Confirmed	Recovered	Deaths
33	2020-02-24	Afghanistan	5	0	0
34	2020-02-25	Afghanistan	5	0	0
35	2020-02-26	Afghanistan	5	0	0
36	2020-02-27	Afghanistan	5	0	0
37	2020-02-28	Afghanistan	5	0	0

let's get data related to italy

df_italy = df[df.Country == 'Italy']

lets check the dataframe

df_italy

	Date	Country	Confirmed	Recovered	Deaths
70185	2020-01-31	Italy	2	0	0
70186	2020-02-01	Italy	2	0	0
70187	2020-02-02	Italy	2	0	0
70188	2020-02-03	Italy	2	0	0
70189	2020-02-04	Italy	2	0	0
...	...	...	...	...	...
70987	2022-04-12	Italy	15404809	0	161032
70988	2022-04-13	Italy	15467395	0	161187
70989	2022-04-14	Italy	15533012	0	161336
70990	2022-04-15	Italy	15595302	0	161469
70991	2022-04-16	Italy	15659835	0	161602

807 rows × 5 columns

let's calculate the infection rate in Italy

df_italy['Infection Rate']= df_italy.Confirmed.diff()
df_italy.head()

C:\Users\ASUS\AppData\Local\Temp/ipykernel_13748/2684023470.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

	Date	Country	Confirmed	Recovered	Deaths	Infection Rate
70185	2020-01-31	Italy	2	0	0	NaN
70186	2020-02-01	Italy	2	0	0	0.0
70187	2020-02-02	Italy	2	0	0	0.0
70188	2020-02-03	Italy	2	0	0	0.0
70189	2020-02-04	Italy	2	0	0	0.0

ok! now let's do the visualization

fig = px.line(df_italy,x='Date',y='Infection Rate', title= 'Before and After Lockdown in Italy ')
fig.add_shape(
    dict(
    type = "line",
    x0 = italy_lockdown_start_date,
    y0 =0, 
    x1 = italy_lockdown_start_date,
    y1 =df_italy['Infection Rate'].max(),
    line = dict(color = 'red' , width=2)
    )

)
fig.add_annotation(
dict(
    x=italy_lockdown_start_date,
    y=df_italy['Infection Rate'].max(),
    text="Starting date of the lockdown"
)
)

fig.add_shape(
    dict(
    type = "line",
    x0 = italy_lockdown_a_month_later,
    y0 =0, 
    x1 = italy_lockdown_a_month_later,
    y1 =df_italy['Infection Rate'].max(),
    line = dict(color = 'orange' , width=2)
    )

)

fig.add_annotation(
dict(
    
    x=italy_lockdown_a_month_later,
    y=0,
    text="A Monte Later of the lockdown")
)

Task 5: Let's See how National Lockdowns Impacts Covid19 active cases in Italy

df_italy.head()

	Date	Country	Confirmed	Recovered	Deaths	Infection Rate
70185	2020-01-31	Italy	2	0	0	NaN
70186	2020-02-01	Italy	2	0	0	0.0
70187	2020-02-02	Italy	2	0	0	0.0
70188	2020-02-03	Italy	2	0	0	0.0
70189	2020-02-04	Italy	2	0	0	0.0

let's calculate number of active cases day by day

df_italy['Deaths Rate'] = df_italy.Deaths.diff()

C:\Users\ASUS\AppData\Local\Temp/ipykernel_13748/3823060142.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

let's check the dataframe again

df_italy.head()

	Date	Country	Confirmed	Recovered	Deaths	Infection Rate	Deaths Rate
70185	2020-01-31	Italy	2	0	0	NaN	NaN
70186	2020-02-01	Italy	2	0	0	0.0	0.0
70187	2020-02-02	Italy	2	0	0	0.0	0.0
70188	2020-02-03	Italy	2	0	0	0.0	0.0
70189	2020-02-04	Italy	2	0	0	0.0	0.0

now let's plot a line chart to compare COVID19 national lockdowns impacts on spread of the virus and number of active cases

fig = px.line(df_italy,x='Date',y=['Infection Rate','Deaths Rate'])

fig.show()

df_italy['Infection Rate'] = df_italy['Infection Rate']/df_italy['Infection Rate'].max()

C:\Users\ASUS\AppData\Local\Temp/ipykernel_13748/23156089.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

df_italy['Deaths Rate'] = df_italy['Deaths Rate']/df_italy['Deaths Rate'].max()

C:\Users\ASUS\AppData\Local\Temp/ipykernel_13748/3670634413.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

fig = px.line(df_italy,x='Date',y=['Infection Rate','Deaths Rate'])
fig.add_shape(
    dict(
    type = "line",
    x0 = italy_lockdown_start_date,
    y0 =0, 
    x1 = italy_lockdown_start_date,
    y1 =df_italy['Infection Rate'].max(),
    line = dict(color = 'green' , width=2)
    )

)
fig.add_annotation(
dict(
    x=italy_lockdown_start_date,
    y=df_italy['Infection Rate'].max(),
    text="Starting date of the lockdown"
)
)

fig.add_shape(
    dict(
    type = "line",
    x0 = italy_lockdown_a_month_later,
    y0 =0, 
    x1 = italy_lockdown_a_month_later,
    y1 =df_italy['Infection Rate'].max(),
    line = dict(color = 'orange' , width=2)
    )

)

fig.add_annotation(
dict(
    
    x=italy_lockdown_a_month_later,
    y=0,
    text="A Monte Later of the lockdown")
)